2025/08/17 by Sagar Shrestha, Rajesh Shrestha, Shrestha, Sagar +5
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Consistency (knowledge bases) #Data Quality and Management #Distribution (mathematics) #Domain (mathematical analysis) #FOS: Computer and information sciences #Flexibility (engineering) #Generative grammar #Latent variable #Machine Learning (cs.LG) #Matching (statistics) #Minification
paper · pdf · doi:10.48550/arxiv.2508.12222
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent advancement in generative models have demonstrated remarkable performance across various data modalities. Beyond their typical use in data synthesis, these models play a crucial role in distribution matching tasks such as latent variable modeling, domain translation, and domain adaptation. Generative Adversarial Networks (GANs) have emerged as the preferred method of distribution matching due to their efficacy in handling high-dimensional data and their flexibility in accommodating various constraints. However, GANs often encounter challenge in training due to their bi-level min-max optimization objective and susceptibility to mode collapse. In this work, we propose a novel approach for distribution matching inspired by the consistency models employed in Continuous Normalizing Flow (CNF). Our model inherits the advantages of CNF models, such as having a straight forward norm minimization objective, while remaining adaptable to different constraints similar to GANs. We provide theoretical validation of our proposed objective and demonstrate its performance through experiments on synthetic and real-world datasets.